Introducing the transitional autonomous vehicle lane-changing dataset: Empirical Experiments

2026-03-05

AI Summary

This study addresses the lack of high-precision trajectory data for characterizing interaction behaviors between transitional autonomous vehicles (tAVs) and human-driven vehicles—or other tAVs—during mandatory lane changes. To bridge this gap, the authors designed two controlled experiments to capture tAV-initiated lane changes and responsive cut-in maneuvers, thereby constructing NC-tALC, the first high-fidelity empirical dataset specifically tailored to tAV lane-changing interactions. Utilizing RTK-GPS for centimeter-level positioning, the experiments recorded 152 trials—including 72 active lane changes and 80 responsive cut-ins—at a 20 Hz sampling rate, integrating adaptive cruise control and multi-tAV coordination mechanisms. The resulting dataset provides a high-quality foundation for modeling tAV interactions, safety evaluation, and traffic flow stability analysis, effectively filling a critical empirical void in the field.

Technology Category

• Humans and AI: Interaction Techniques and Devices
• Planning, Routing, and Scheduling: Activity and Plan Recognition
• Intelligent Robots: Behavior Learning & Control

Application Category

• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Security and Privacy: Data transparency and provenance

Abstract

Transitional autonomous vehicles (tAVs), which operate beyond SAE Level 1-2 automation but short of full autonomy, are increasingly sharing the road with human-driven vehicles (HDVs). As these systems interact during complex maneuvers such as lane changes, new patterns may emerge with implications for traffic stability and safety. Assessing these dynamics, particularly during mandatory lane changes, requires high-resolution trajectory data, yet datasets capturing tAV lane-changing behavior are scarce. This study introduces the North Carolina Transitional Autonomous Vehicle Lane-Changing (NC-tALC) Dataset, a high-fidelity trajectory dataset designed to characterize tAV interactions during lane-changing maneuvers. The dataset includes two controlled experimental series. In the first, tAV lane-changing experiments, a tAV executes lane changes in the presence of adaptive cruise control (ACC) equipped target vehicles, enabling analysis of lane-changing execution. In the second, tAV responding experiments, two tAVs act as followers and respond to cut-in maneuvers initiated by another tAV, enabling analysis of follower response dynamics. The dataset contains 152 trials (72 lane-changing and 80 responding trials) sampled at 20 Hz with centimeter-level RTK-GPS accuracy. The NC-tALC dataset provides a rigorous empirical foundation for evaluating tAV decision-making and interaction dynamics in controlled mandatory lane-changing scenarios.

Problem

• transitional autonomous vehicles
• lane-changing
• trajectory dataset
• mandatory lane changes
• vehicle interaction

Innovation

• transitional autonomous vehicles
• lane-changing dataset
• trajectory data
• human–autonomy interaction
• mandatory lane change